( PDF ) Estoques mistos da tartaruga-verde (Chelonia mydas) no sul do Brasil, revelados por dna mitocondrial e correntes oceânicas

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MAÍRA CARNEIRO PROIETTI

ESTOQUES MISTOS DA TARTARUGA-VERDE (Chelonia

mydas) NO SUL DO BRASIL, REVELADOS POR DNA

MITOCONDRIAL E CORRENTES OCEÂNICAS

RIO GRANDE

Junho de 2009

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UNIVERSIDADE FEDERAL DO RIO GRANDE

INSTITUTO DE OCEANOGRAFIA

PÓS-GRADUAÇÃO EM OCEANOGRAFIA BIOLÓGICA

ESTOQUES MISTOS DA TARTARUGA-VERDE (Chelonia

mydas) NO SUL DO BRASIL, REVELADOS POR DNA

MITOCONDRIAL E CORRENTES OCEÂNICAS

MAÍRA CARNEIRO PROIETTI

Dissertação apresentada ao Programa de

Pós-graduação em Oceanografia

Biológica da Universidade Federal do Rio

Grande, como requisito parcial à obtenção

do título de MESTRE.

Orientador: Eduardo Resende Secchi

Co-orientador: Luis Fernando Marins

RIO GRANDE

Junho de 2009

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iii

AGRADECIMENTOS

Dedico este trabalho aos meus pais, que sempre me apoiaram

incondicionalmente e me inspiram a querer ser uma pesquisadora melhor. Agradeço a

Júlia, grande amiga e colega de trabalho, por tudo. Obrigada pela amizade sem

tamanho, ajuda, força, discussões, e todo o aprendizado ao longo dos anos. Agradeço

aos orientadores, Edu e Luf, pela ajuda e confiança ao longo do trabalho. Agradeço o

Conselho Nacional de Pesquisa e Desenvolvimento (CNPq) pela bolsa de mestrado, e

as instituições britânicas Rufford Small Grants (RSG) e People‟s Trust for Endangered

Species (PTES) pelo financiamento do trabalho. Obrigada a todos do Laboratório de

Biologia Molecular da FURG, em especial Dani, Liane e Lupe pela amizade,

companheirismo e enorme apoio tanto dentro quanto fora do laboratório, e Márcio pelas

inúmeras consultorias laboratoriais. A todos que participaram das expedições ao

Arvoredo, não tenho palavras para agradecer todo o trabalho e os “perrengues”

enfrentados: Déa, Mari, Manu, Tremembé, Diego, Arthur, Theo, Curiri, Tiago, Pablo,

Jaca, Paulinha, Chris, Japa, Alessandra, Robs, Arnaldinho. Obrigada a todas as

operadoras de mergulho (Pata da Cobra, Parcel, Sea Divers, Aquanauta), e um

agradecimento especial ao Neio, Tiago e Tidico da Pata da Cobra, pela grande força.

Agradeço a Marinha do Brasil pela disposição em nos apoiar na ilha. Ao NEMA e

CRAM, em especial Dani, Serginho e Alice, agradeço o fornecimento de material da

Praia do Cassino. Ao Fabrício Santos, Sarah Vargas e o Laboratório de Biologia e

Evolução Molecular da UFMG, agradeço a parceria e ajuda com seqüenciamento, e a

Marina Lobato e a Fundação Hemominas pela ajuda com PCRs. Muito obrigada ao

Paul Kinas pela valiosa ajuda na construção das prioris e ao Tiago Grandra pelo mapa

das áreas. Agradeço imensamente todos os amigos queridos, sempre presentes

mesmo de longe, que acompanharam esta trajetória: Vi, Tati, Cá, Cami, Tiago, Mari,

Kássio, Robs, Renatão, O Bando de Coirana, Amália, Shei, Cissa, Matas. Obrigada

também aos pentagêmeos pelas boas risadas, e ao Chris por todo o carinho e apoio na

reta final. Muito obrigada a todos!

ÍNDICE

RESUMO ......................................................................................................................... 1

ABSTRACT ..................................................................................................................... 2

I) INTRODUÇÃO ............................................................................................................. 3

I.1.) A tartaruga-verde .................................................................................................. 3

I.2.) Ciclo de vida ......................................................................................................... 4

I.3.) Marcadores genéticos aplicados ao estudo de tartarugas-verdes ........................ 6

I.4.) Análise Bayesiana de Estoque Misto .................................................................... 8

I.5.) Correntes e dispersão de tartarugas marinhas ..................................................... 9

I.6.) Implicações para a conservação......................................................................... 11

I.7.) Objetivos ............................................................................................................. 11

II) MATERIAL E MÉTODOS .......................................................................................... 12

III) RESULTADOS ......................................................................................................... 13

IV) CONSIDERAÇÕES FINAIS ..................................................................................... 15

V) LITERATURA CITADA .............................................................................................. 16

VI) FIGURAS ................................................................................................................. 23

VII) ANEXOS ................................................................................................................. 26

VIII) APÊNDICE: MANUSCRITO: formatado para o periódico Molecular Ecology. ....... 28

RESUMO

Análises genéticas podem elucidar diversos aspectos da biologia e ecologia de

tartarugas-verdes (Chelonia mydas), como composição de áreas de alimentação,

dispersão de filhotes e migrações. Para avaliar estrutura genética e estimar origens

natais de áreas de alimentação no sul do Brasil, seqüências do DNA mitocondrial da

Ilha do Arvoredo (SC – n = 115) e Praia do Cassino (RS – n = 101) foram analisadas e

comparadas a outras áreas de forrageio e desova do Oceano Atlântico. Para comparar

estimativas de origens natais (obtidas através da Análise Bayesiana de Estoque Misto)

com dados oceanográficos e desenvolver novas prioris informativas para estas

estimativas, trajetórias de bóias de deriva do Oceano Atlântico foram analisadas. Cada

área de estudo apresentou doze haplótipos, dos quais dez eram compartilhados e

presentes em freqüências extremamente semelhantes. Os haplótipos mais freqüentes

nas amostras foram CM-A8 e CM-A5 (aproximadamente 60% e 20%, respectivamente),

e haplótipos restantes apresentaram freqüências menores que 5%. Não foi observada

estruturação genética entre a as áreas de estudo. A Ilha do Arvoredo e Praia do

Cassino também não apresentaram estruturação em relação a Ubatuba e

Rocas/Noronha, no Atlântico sudoeste, mas sim em relação a áreas de alimentação

mais distantes no Brasil, Caribe e América do Norte. A análise de trajetórias de bóias de

deriva revelou que bóias provindas das ilhas Ascensão e Trindade são dominantes na

costa leste brasileira. As prioris desenvolvidas para a Análise de Estoque Misto não

alteraram muito estimativas de contribuições, porém as consideramos ecologicamente

mais realistas. As Ilhas de Ascensão, Aves e Trindade, assim como o Golfo de Guiné,

foram os principais contribuintes para os estoques mistos do sul do Brasil. No entanto,

áreas de desovam necessitam de uma caracterização genética mais adequada para

fornecer estimativas mais precisas de origens natais, uma vez que a redução de

impactos em estoques mistos ao longo da costa resulta na conservação de estoques

reprodutivos freqüentemente localizados a grandes distâncias.

Palavras-chave: tartaruga-verde, Atlântico sudoeste, áreas de alimentação, genética,

análise de estoque misto, bóias de deriva.

ABSTRACT

Genetic analyses have the potential to elucidate many aspects of juvenile green

turtle (Chelonia mydas) biology and ecology, such as foraging ground composition,

hatchling dispersal and migrations. To evaluate genetic structure and assess natal

origins of mixed stocks in Southern Brazil, we analyzed mitochondrial DNA control

region sequences from Arvoredo Island (n = 115) and Cassino Beach (n = 101),

comparing them to other mixed stocks and examining their composition in terms of

stocks (nesting areas) in the Atlantic Ocean. In order to compare natal origin estimates

(obtained through Bayesian Mixed Stock Analysis) with oceanographic data and develop

novel informative priors for this analysis, surface drifter trajectories in the Atlantic Ocean

were analyzed. Each study area presented twelve haplotypes, of which ten were shared

at extremely similar frequencies. Haplotypes CM-A8 and CM-A5 were most frequent,

representing respectively around 60% and 20% of samples from both areas, and

remaining haplotypes presented frequencies lower than 5%. Genetic structuring was not

observed between the study areas. Arvoredo Island and Cassino Beach also did not

present structuring in relation to Ubatuba and Rocas/Noronha, in the southwestern

Atlantic, but were structured when compared to farther feeding areas in Brazil, the

Caribbean, and North America. Analysis of drifter trajectories revealed that drifters from

Ascension and Trindade Islands are dominant at the eastern coast of Brazil. Informative

priors developed for Mixed Stock Analysis did not greatly alter stock estimates; we do,

however, consider them to be ecologically more realistic. Ascension, Aves and Trindade

Islands, as well as Gulf of Guinea, were the main contributors to the Southern Brazil

mixed stocks. However, rookeries require adequate genetic characterization in order to

provide accurate estimated of natal origins, an analysis with important implications for

the survival of this species, since the reduction of impacts on mixed stocks along the

coast will ultimately lead to the conservation of reproductive stocks frequently thousands

of kilometers away.

Keywords: green turtle, southwestern Atlantic, foraging grounds, genetics, mixed stock

analysis, surface drifters

I) INTRODUÇÃO

I.1.) A tartaruga-verde

A tartaruga-verde (Chelonia mydas) pertence à ordem Testudinata, subordem

Cryptodira, superfamília Chelonioidea, família Cheloniidae. Possui casco ósseo rígido e

oval, de coloração predominantemente marrom e verde, geralmente apresentando

padrões radiais e rajados. As características morfológicas que a distinguem das demais

espécies são: quatro pares de escudos laterais no casco, quatro pares de escamas

pós-orbitais, duas escamas pré-frontais alongadas entre as orbitais e uma mandíbula

serrilhada (Pritcher & Mortimer 1995; Figura 1). Tamanhos e pesos variam de acordo

com a população, podendo chegar a 140 cm de comprimento de casco e 300 kg (Carr

1952), porém geralmente não ultrapassam os 123 cm e 240 kg (Pritcher & Mortimer

1995). No Brasil, um estudo com tartarugas-verdes adultas no Atol das Rocas revelou

tamanhos e pesos médios diferenciados entre machos e fêmeas, com média de 106 cm

e 134 kg (máximo 118 cm e 176 kg) para machos e 113 cm e 147 kg (máximo 130 cm e

191 kg) para fêmeas (Grossman et al. 2007). Esta espécie possui hábito alimentar

onívoro com tendência à herbivoria, variando de acordo com suas fases de vida e

necessidades nutricionais e energéticas (Bjorndal 1985, Bjorndal 1997). Apresenta

distribuição circumglobal tropical e subtropical e complexo ciclo vital, ocupando diversos

nichos ecológicos e habitats distintos ao longo de sua vida, e realizando migrações

muitas vezes de larga-escala entre áreas de alimentação e reprodução quando atingem

a maturidade sexual (Meylan & Meylan 1999, Bolten 2003, Godley et al. 2003). No

Brasil, esta espécie ocorre em todo o litoral para alimentação e desenvolvimento, mas

sua reprodução é restrita às ilhas oceânicas de Trindade, Atol das Rocas e Fernando

de Noronha (Bellini & Sanches 1996, Grossman et al. 2007). Assim como as outras seis

espécies viventes de tartarugas marinhas, encontra-se ameaçada de extinção (IUCN

2009) devido a fatores como superexploração, ocupação desordenada do litoral e

destruição de habitats, poluição marinha, e captura acidental em pesca (Limpus 1995,

Campbell 2003, Bugoni et al. 2001, Domingos et al. 2006).

I.2.) Ciclo de vida

Tartarugas-verdes atingem a maturidade sexual entre 20 e 50 anos de idade, e

apresentam comportamento reprodutivo filopátrico, ou seja, retornam à mesma região

para reproduzirem em ciclos reprodutivos subseqüentes (Carr 1967, Miller 1997, Avise

2007). A corte e a cópula geralmente ocorrem em águas próximas às praias de desova,

sendo comum o cortejo de uma única fêmea por vários machos (Limpus 1993; Miller

1997). Aproximadamente 20 dias após a cópula, a fêmea sobe a praia para desovar,

depositando geralmente entre 100 e 120 ovos em uma câmara cavada com as

nadadeiras (Miller 1997). Desovam de três a sete vezes por temporada reprodutiva, e

ovos permanecem incubados por 50-60 dias, período após o qual ocorre a eclosão e a

corrida dos filhotes para o mar, onde nadam contra as ondas para chegarem ao mar

aberto, onde o risco de predação é supostamente menor (Carr 1967, Musick & Limpus

1997). Pouco se sabe sobre a fase pós-eclosão destes animais, em que permanecem

no ambiente pelágico sendo, teoricamente, transportados por correntes oceânicas

durante alguns anos (“os anos perdidos” – Carr 1967, Witham 1980, Musick & Limpus

1997). Após atingirem tamanho adequado (geralmente entre 20 e 35 cm de carapaça

de acordo com o local e população; Bjorndal 1997, Musick & Limpus 1997, Bolten

2003), juvenis recrutam para áreas costeiras de alimentação e desenvolvimento, onde

há mistura de indivíduos provindos de diversas áreas reprodutivas formando um

“estoque misto” (Bass et al. 2006). As tartarugas-verdes muitas vezes exibem fidelidade

a estes locais, mas podem realizar deslocamentos entre diferentes áreas (Godley et al.

2003, Reisser et al. 2008). Quando se aproximam da maturidade sexual, podem se

mover para áreas de alimentação adultas (Godley et al. 2003), possivelmente devido à

maior proximidade de áreas de desova ou necessidades alimentares. Ao atingirem a

maturidade, se deslocam para suas áreas reprodutivas, retornando à área de

alimentação após a reprodução, onde permanecem entre dois e seis anos (fêmeas) e

um a dois anos (machos) armazenando energias até o próximo ciclo reprodutivo

(Musick & Limpus 1997, Hamman et al. 2003). A Figura 2 apresenta o modelo geral

mais aceito do ciclo de vida das tartarugas-verdes.

I.3.) Marcadores genéticos aplicados ao estudo de tartarugas-verdes

A complexidade do ciclo de vida destes animais e as grandes distâncias

geográficas e temporais envolvidas tornam o estudo direto destes animais difícil, e

abordagens indiretas através de análises moleculares podem elucidar muitos aspectos

de sua biologia e comportamento (Bowen 1995, Bowen & Karl 1997, Bowen & Karl

2007, Avise 2007). Marcadores moleculares foram utilizados para investigar sistemas

de acasalamento e paternidade de tartarugas-verdes, e confirmaram a suspeita de

poligamia e múltipla paternidade de ninhos (FitzSimmons 1998, Lee & Hays 2004).

Estrutura populacional e fluxo gênico foram avaliados com marcadores mitocondriais e

nucleares, evidenciando estruturação entre áreas de desova com baixo fluxo gênico

entre elas, ou seja, baixas taxas de migrações entre áreas de desova (Roberts et al.

2004). Estudos moleculares foram aplicados também à filogeografia, revelando

separação genética entre Oceano Atlântico/Mediterrâneo e Oceano Pacífico/Índico,

assim como elevada estruturação entre quatro grupos regionais de áreas de desova no

Oceano Atlântico: um grupo a oeste, composto pelo México, Costa Rica e Flórida; um

central, envolvendo a Ilha Aves e Suriname; um ao sul/sudeste, composto pelo Brasil,

Ilha de Ascensão e Guiné Bissau; e um ao leste em Chipre, no Mediterrâneo (Bowen et

al. 1992, Encalada et al. 1996, Lahanas et al. 1998). Sistemática e taxonomia também

são focos de estudos genéticos. No Pacífico Leste, uma variação morfológica da

tartaruga-verde denominada tartaruga-negra é considerada por muitos pesquisadores

uma espécie distinta (Chelonia agassizii); porém, análises genéticas não evidenciaram

esta distinção, e embora as tartarugas-negras sejam uma população com morfologia

característica e limitada distribuição geográfica, não devem ser consideradas uma

espécie geneticamente distinta (Karl & Bowen 1999). Por fim, a hipótese de retorno à

praia natal e origens natais de indivíduos em fases não-reprodutivas do ciclo de vida,

também são foco de estudos genéticos (Avise 2007).

A hipótese de “retorno à praia natal” foi proposta pela primeira vez por Carr

(1967), baseada na observação de que tartarugas-verdes fêmeas apresentam elevado

grau de filopatria, com variados graus de precisão (Miller 1997, Musick & Limpus 1997,

Formia et al. 2007, Avise 2007, Lee et al. 2007). Diante desta observação, surgiram

duas hipóteses para explicar a fidelidade de fêmeas ao sítio reprodutivo: “retorno à

praia natal”, em que as fêmeas retornariam à região em que nasceram para se

reproduzirem, e “facilitação social”, em que fêmeas inexperientes seguiriam as

experientes para um local de desova e utilizariam este local para sua reprodução dali

em diante (Bowen & Karl 2007). Estas hipóteses são difíceis de testar diretamente,

considerando a dificuldade de se aplicar uma marca a um filhote recém-nascido de 5

cm e recuperá-la décadas depois, em um adulto medindo mais de um metro. Porém,

estas hipóteses geram predições testáveis da estruturação genética entre populações:

se uma fêmea retorna fielmente à sua área natal, cada área de desova apresentará

uma assinatura genética única em termos do DNA mitocondrial herdado maternamente;

por outro lado, se as áreas de desova são escolhidas por facilitação social, haverá alta

taxa de fluxo gênico entre populações de desova que se sobrepõem em locais de

alimentação (Bowen & Karl 2007). Estudos de marcação de tartarugas-verdes

demonstraram que locais de alimentação no nordeste brasileiro são divididos por

animais de populações de desova do Suriname e da Ilha de Ascensão. Apesar da

sobreposição em áreas de alimentação, estudos genéticos demonstraram grande

diferenciação genética entre amostras do Suriname e Ascensão, revelando que não há

dispersão de fêmeas entre estas áreas e apoiando a hipótese de retorno à praia natal

(Bowen et al. 1992, Allard et al. 2004, Bowen & Karl 2007), tendência observada

também para a Austrália (Dethmers et al. 2006).

I.4.) Análise Bayesiana de Estoque Misto

Baseada na premissa de que existe estruturação entre áreas de desova, uma

abordagem Bayesiana denominada Análise de Estoque Misto (Mixed Stock Analysis –

MSA) tem sido crescentemente aplicada para a determinação da contribuição de áreas

de desova (estoques) para agregações alimentares de tartarugas (estoques mistos).

Esta análise, inicialmente desenvolvida para a avaliação de estoques pesqueiros, utiliza

diferenças nas freqüências de caracteres genéticos (especialmente haplótipos de

mtDNA) para estimar contribuições de cada estoque para um estoque misto (Pella &

Masuda 2001). Esta análise freqüentemente apresenta elevados desvios-padrão e é

baseada no pressuposto de que todos os estoques e misturas são adequadamente

amostrados, o que não é o caso, com muitas áreas apresentando pouco ou nenhuma

caracterização genética (Avise 2007). Apesar destas potenciais fontes de erro, a MSA é

extremamente útil para estimativas quantitativas e descrições qualitativas de origens de

tartarugas-verdes em habitats de alimentação (Bowen & Karl 2007). Estas estimativas

devem ser consideradas com cuidado e, quando possível, comparadas e associadas a

outros dados e hipóteses. A abordagem Bayesiana para a MSA permite incorporar

prioris informativas para melhorar estimativas, e dados ecológicos como tamanho da

população de desova e distância do estoque ao estoque misto são comumente

empregados, com base no pressuposto de que estes fatores influenciam a composição

do estoque misto. Dados oceanográficos, como trajetórias de derivadores superficiais,

podem ser bons indicadores das dispersões de tartarugas marinhas recém-nascidas,

visto que estas são consideradas pela maioria dos pesquisadores como “pelágicas” e

transportadas por correntes oceânicas (Bolten 2003), possuindo deste modo grande

potencial para incorporação nas MSAs Bayesianas.

Alguns exemplos de estimativas de origens natais de tartarugas-verdes do

Atlântico e Pacífico podem ser vistos nos trabalhos de Bass et al. (2006), Bolker et al.

(2007), Bjorndal & Bolten (2008), e Dutton et al. (2008). No Atlântico sudoeste e sul-

central existem quatro áreas de desova de tartarugas-verdes, listadas em ordem

decrescente de tamanho populacional: Ilha de Ascensão (Reino Unido), Ilha de

Trindade (ES), Atol das Rocas (RN) e Fernando de Noronha (PE). Origens de

tartarugas-verdes juvenis do Brasil foram descritas para o Atol das Rocas (RN) e

Fernando de Noronha (PE) (agrupados em uma só área em Bjorndal et al. 2006)

Ubatuba (SP) e Almofala (CE) (Naro-Maciel et al. 2007). Para todas estas áreas, foram

consistentemente observadas contribuições altas da Ilha de Ascensão, seguidas por

menores porém importantes contribuições da Ilha de Trindade e contribuições quase

nulas do Atol das Rocas e Fernando de Noronha. Estes autores atribuem estas

estimativas de contribuição ao padrão de correntes oceânicas que fluem próximo a

estas áreas de desova.

I.5.) Correntes e dispersão de tartarugas marinhas

O papel das correntes oceânicas na dispersão e migração de tartarugas

marinhas tem sido amplamente discutido (ver Luschi et al. 2003a). Como citado

anteriormente, acredita-se que tartarugas recém-nascidas são dispersas por correntes

oceânicas até recrutarem para suas áreas de alimentação costeiras, e o

acompanhamento de tartarugas através de telemetria por satélite indicam que os

movimentos e migrações destes animais são freqüentemente moldados por correntes

oceânicas (Luschi et al. 1998, 2003a, 2003b; Craig et al. 2004). Para estoques mistos

do Atlântico norte, comparações entre MSA e correntes oceânicas foram realizadas por

Luke et al. (2004) e Bass et al. (2006), e estes autores concluíram que a composição

destas áreas de alimentação dependem dos sistemas locais de correntes oceânicas.

Para o Brasil, foi sugerido que recém-nascidos da Ilha de Ascensão derivam com

correntes Equatorias na direção da América do Sul, enquanto recém-nascidos de outras

áreas de desova possivelmente derivam em outras direções com as correntes

dominantes (Naro-Maciel et al. 2007). A circulação geral de larga escala da camada

superficial (< 100 m) do Oceano Atlântico Sul, que possivelmente influencia a dispersão

de tartarugas marinhas e conseqüentemente afeta a composição de áreas de

alimentação brasileiras, é caracterizada por um giro anticiclônico subtropical dominante.

A Corrente Sul Equatorial (CSE) flui para o oeste, bifurcando quando atinge a

plataforma continental sul-americana a aproximadamente 10° S, originando a Corrente

Norte do Brasil, com direção norte, e a Corrente do Brasil (CB), com direção sul. A CB

flui para o sul ao longo da costa até atingir a Zona de Convergência Subtropical

(aproximadamente 33-38º S), onde encontra a Corrente das Malvinas e separa da costa

formando a Corrente Sul do Atlântico, que flui para o leste. Ao se aproximar da costa

africana, parte flui para o Oceano Índico e parte forma a Corrente de Benguela que viaja

para o norte ao longo da costa, formando por sua vez a CSE e fechando o giro

(Stramma & England 1999) (Figura 3).

I.6.) Implicações para a conservação

A relevância conservacionista em se identificar origens natais e rotas de

dispersão de estoques mistos está no fato de que áreas de desova, apesar de serem

geralmente independentes em termos reprodutivos, são conectadas em fases não-

reprodutivas do ciclo de vida (Avise 2007). Impactos em áreas de alimentação e rotas

migratórias podem afetar ao mesmo tempo múltiplos estoques em diferentes níveis, de

acordo com o tamanho populacional do estoque e a fração de seus potenciais

reprodutores que habita uma determinada área de alimentação. A determinação de

rotas migratórias e identificação das áreas de desova impactadas com a mortalidade

em áreas não-reprodutivas, provocada por exploração humana direta e captura

acidental na pesca, podem auxiliar no manejo prático de populações (Avise 2007).

Portanto, o entendimento das origens natais e a determinação de possíveis rotas de

deslocamento entre áreas de desova e alimentação são essenciais para a identificação

de unidades discretas para a elaboração de planos de manejo e conservação de

tartarugas marinhas (Moritz 1994, Avise 2007, Bowen & Karl 2007).

I.7.) Objetivos

Considerando que estudos genéticos possuem o potencial de iluminar vários

aspectos da biologia e ecologia de tartarugas marinhas, incluindo composição de áreas

de alimentação, dispersão de tartarugas recém-nascidas e migrações, este trabalho

estudou haplótipos da região controle do DNA mitocondrial de tartarugas-verdes de

duas áreas de alimentação sul-brasileiras, a Ilha do Arvoredo (SC) e a Praia do Cassino

(RS), com os objetivos de: 1) diferenciar geneticamente as áreas de estudo das demais

áreas de alimentação do Atlântico com caracterização genética; 2) elucidar origens

natais das tartarugas se alimentando nas áreas de estudo; 3) desenvolver novas prioris

informativas para a Análise Bayesiana de Estoque Misto; 4) verificar o efeito da

incorporação de diferentes prioris informativas na Análise Bayesiana de estoque misto;

e 5) determinar possíveis padrões de dispersão de recém-nascidos das áreas de

desova para as áreas estudadas.

II) MATERIAL E MÉTODOS

Foram analisadas 115 amostras da Ilha do Arvoredo, de tartarugas-verdes com

comprimento curvilíneo de carapaça (CCC) variando de 33.5-83 cm (média 49.2 cm), e

101 amostras da Praia do Cassino, de animais com CCC variando de 29-71.5 cm

(média 40.1 cm). A Ilha do Arvoredo, maior ilha da Reserva Biológica Marinha do

Arvoredo, é um recife rochoso coberto predominantemente por algas de clima tropical e

temperado quente (Horta et al. 2008), com elevada ocorrência de tartarugas-verdes

(Reisser et al. 2008) (Figura 4). A Praia do Cassino é uma extensa (aproximadamente

350 km) e contínua praia arenosa, cujo substrato é predominantemente não-

consolidado (Figura 4). Nesta área há freqüente ocorrência da tartaruga-verde (Bugoni

et al. 2001), embora o papel exato deste local no ciclo de vida destes animais não seja

conhecido devido à ausência de estudos de marcação e recaptura e telemetria. DNA

genômico foi extraído de amostras de tecido através do método padrão de extração

fenol:clorofórmio (adaptado de Hillis et al. 1996, ver Anexo 1). Fragmentos de

aproximadamente 490 pares de base (pb) da região controle do mtDNA foram

amplificados via Reação em Cadeia da Polimerase (PCR), utilizando-se primers

específicos. Produtos da PCR foram purificados e sequenciados em ambas as direções.

Seqüências foram classificadas de acordo com haplótipos previamente descritos para

tartarugas-verdes do Oceano Atlântico, e as relações entre haplótipos encontrados

foram demonstradas através de uma rede haplotípica. Testes de diferenciação e

AMOVA foram efetuados para verificar estruturação entre as áreas de estudo e outras

áreas de alimentação do Oceano Atlântico com descrição genética.

Para comparar resultados da MSA com dados oceanográficos e desenvolver

duas novas prioris informativas para esta análise, trajetórias de bóias de deriva do

Oceano Atlântico foram obtidas do programa de bóias de deriva da NOAA

(http://www.aoml.noaa.gov/envids/gld). O número de bóias que passaram por

determinadas áreas (áreas de desova de tartarugas-verdes incluídas na MSA) e a

fração destas bóias que chegaram a uma área-alvo na costa brasileira foram

determinados. Deste modo, a probabilidade posterior de uma bóia que chegou à área-

alvo ser de determinada área de desova foi calculada em um contexto Bayesiano,

seguindo uma distribuição Beta. Prováveis origens natais foram determinadas através

de Análise Bayesiana de Estoque Misto, considerando prioris não-informativas (MSA

) e

empregando prioris informativas de acordo com: 1) as probabilidades estimadas a partir

de dados de bóias de deriva (MSA

); 2) o número de fêmeas/ano de cada área-fonte

(MSA

); e 3) combinando as duas prioris informativas (MSA

). Estoques reprodutivos

considerados como possíveis contribuintes para as áreas de estudo correspondem a

todas as áreas de desova do Atlântico com descrição do DNA mitocondrial.

III) RESULTADOS

Cada área de estudo apresentou 12 haplótipos previamente descritos, dos quais

10 foram compartilhados (CM-A5, CM-A6, CM-A8, CM-A9, CM-A10, CM-A23, CM-A24,

CM-A32, CM-A42 e CM-A45) e quatro não (CM-A3 e CM-A39, presentes somente na

Ilha do Arvoredo, e CM-A25 e CM-A36, presentes somente na Praia do Cassino). O

número de sítios polimórficos definindo estes haplótipos foi 19 para ambas as áreas,

com um máximo de 12 variações distinguindo-os. As duas áreas foram caracterizadas

pela predominância dos haplótipos CM-A8 (61%) e CM-A5 (22% e 20% para a Ilha do

Arvoredo e Praia do Cassino, respectivamente), e freqüências menores que 5% para os

haplótipos restantes. Diversidades haplotípicas (h) e nucleotídicas (π) foram

extremamente similares, apresentando os valores respectivos de 0,5831 ± 0,0451 e

0,00246 ± 0,00176 para a Ilha do Arvoredo e 0,5857 ± 0,0501 e 0,00251 ± 0,00178 para

a Praia do Cassino.

Não foi constatada estruturação genética entre as áreas de estudo, com um Φ

ligeiramente negativo (Φ

= -0,0066, p > 0,05), indicando que o valor real é porém

muito baixo (Weir 1996). As análises revelaram que a Ilha do Arvoredo e a Praia do

Cassino são geneticamente diferentes em termos de haplótipos de mtDNA da maioria

das áreas de comparação (Almofala, Nicarágua, Barbados, Carolina do Norte, Florida e

Bahamas, p < 0,05), mas não apresentam diferença em relação a Ubatuba e

Rocas/Noronha (p > 0,05), localizadas no Atlântico sudoeste.

As trajetórias de todas as bóias de deriva que passaram pelas 11 áreas 4°x4°

(todas as áreas de desova consideradas nas MSAs) revelaram que bóias provindas de

Ascensão e Trindade são dominantes na costa leste brasileira.

Todas as MSAs demonstraram que os principais contribuintes para as áreas de

alimentação sul-brasileiras são as Ilhas de Ascensão, Aves e Trindade. A Ilha de

Ascensão consistentemente mostrou maiores contribuições, acima de 50% em todas as

análises, enquanto as Ilhas Aves e Trindade geralmente exibiram contribuições acima

de 21 e 13%, respectivamente. Os estoques restantes apresentaram baixas

contribuições (geralmente menos de 1%), com a exceção do Golfo de Guiné, que

alcançou contribuições acima de 10% em algumas análises. A inserção de prioris

ecológicas não alterou muito estimativas da MSA. Para resultados detalhados e

discussão referir-se ao Apêndice 1 deste trabalho.

IV) CONSIDERAÇÕES FINAIS

A Ilha do Arvoredo e Praia do Cassino apresentaram diversidade e freqüências

haplotípicas extremamente semelhantes, e não foi observada estruturação genética

significativa entre elas e outras áreas de alimentação do Atlântico sudoeste

relativamente próximas (Ubatuba – SP e Rocas/Noronha – PE). Entretanto, há

estruturação significativa em relação a Almofala (CE), uma área de alimentação mais

distante. Trajetórias de bóias de deriva revelaram que as Ilhas de Ascensão e Trindade,

e em menor proporção Golfo de Guiné e Rocas/Noronha, apresentam condições

favoráveis para conduzir bóias de deriva à costa brasileira. As Análises de Estoque

Misto mostraram que o principal estoque contribuindo para as áreas de estudo foi a Ilha

de Ascensão, seguido pela Ilha Aves, Ilha de Trindade, e Golfo de Guiné. A inserção de

prioris ecológicas não provocou grandes alterações nas estimativas das MSAs. No

presente trabalho foram combinados diferentes tipos de dados ecológicos para

incorporação nas análises de origens natais, e acreditamos que esta combinação é o

cenário ideal para se obter estimativas de contribuições mais realistas. Apesar de

serem unidades geneticamente indistinguíveis, e serem agrupadas para a Análise de

Estoque Misto, sugerimos que diferentes estratégias de manejo sejam adotadas devido

a algumas diferenças entre as áreas em termos das tartarugas-verdes ocorrentes e sua

utilização dos habitats. O tamanho médio de carapaça dos animais amostrados neste

trabalho demonstra que as tartarugas da Ilha do Arvoredo e Praia do Cassino estão em

estágios diferentes de seus ciclos vitais, com a Praia do Cassino apresentando animais

menores. Estes diferentes estágios possuem diferentes vulnerabilidades e sua

sobrevivência influencia diferentemente taxas de crescimento populacional, com juvenis

maiores, por seu maior valor reprodutivo, contribuindo mais do que indivíduos menores

(Crouse et al. 1987; Crouse et al. 1999). Além disso, estes habitats podem apresentar

diferentes papéis no ciclo de vida das tartarugas. Enquanto fidelidade em pelo menos

curto prazo já foi observada para a Ilha do Arvoredo (Reisser et al. 2008), o papel da

Praia do Cassino ainda não é claro devido à falta de estudos de marcação e recaptura e

telemetria, e é possivelmente mais importante como um corredor migratório do que uma

área de alimentação, quando comparada à Ilha do Arvoredo. Impactos nos estágios de

desenvolvimento das tartarugas-verdes podem impedi-las de cumprir seu papel

ecológico de atingir a maturidade e se reproduzir. Portanto, a proteção de juvenis na

costa leva à conservação de estoques reprodutivos muitas vezes localizados a milhares

de quilômetros de distância. A identificação de contribuições de estoques para áreas de

alimentação possui importantes implicações para a conservação, e para ser

considerada como uma ferramenta para a elaboração adequada de planos de manejo e

conservação, é necessário que se descreva adequadamente o mtDNA das populações

de desova, de modo a melhorar dados genéticos de base utilizados nas estimativas de

origens natais.

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VI) FIGURAS

Figura 1. Morfologia externa das tartarugas-verdes (Chelonia mydas), com

destaque para as características que as diferenciam como espécie. Adaptado de

Pritcher & Mortimer 1995.

Figura 2. Ciclo de vida geral das tartarugas-verdes. Adaptado de Miller 1997.

Figura 3. Sistema de correntes superficiais do Oceano Atlântico sul. Retirado de

Stramma & England 1999.

Figura 4. Localização geográfica da Ilha do Arvoredo (esquerda) e Praia do

Cassino (direita).

VII) ANEXOS

Anexo 1. Protocolo de extração do DNA (adaptado de Hillis et al. 1996)

1- Retirar porção branca do tecido animal (aproximadamente 0,5 g) e depositar em

tubo eppendorf para hidratação com água destilada, durante 10 minutos;

2- Descartar água e adicionar 500 µl de solução tampão (composição abaixo),

macerando o tecido para início da lise celular;

3- Adicionar 20 µl de proteinase K e levar a estufa (56°C), para catálise da digestão

protéica celular. Deixar por 4 horas;

4- Para início da separação entre porção orgânica (contendo moléculas indesejadas

como proteínas, lipídeos, etc.) e hidrossolúvel (contendo DNA), adicionar 250µl

fenol (Buffer Saturated Phenol – Invitrogen) e 250µl clorofórmio/isoamil (24:1);

5- Agitar solução obtida à mão por quinze minutos;

6- Centrifugar por quinze minutos a 12000 rotações por minuto (RPM) para

formação de duas fases distintas: a porção orgânica, mais densa, compondo a

fase inferior; e a hidrossolúvel, formando um sobrenadante, com fina película

entre elas;

7- Retirar sobrenadante era então retirado (com cuidado para não romper a película

entre fases) em três vezes de 150µl e depositar em novo eppendorf;

8- Novamente adicionar solução clorofórmio/isoamil (24:1, 500µl) e agitar

manualmente por dez minutos;

9- Centrifugar por dez minutos a 12000 RPM para novamente formar duas fases;

10- Retirar a fase superior retirada em duas vezes de 150µl e depositar em

eppendorf vazio;

11- Depositar juntamente ao sobrenadante 250µl acetato de amônio (7,5M) e 1000µl

etanol absoluto gelado;

12- Colocar a solução em freezer a -20°C por 30 minutos (ou overnight) para

precipitação do DNA;

13- Retirar a amostra do freezer e submetê-la a centrifugação (15000 RPM por vinte

minutos) para formação do pellet de DNA;

14- Descartar a porção aquosa, remanescendo somente o pellet no fundo tubo;

15- Lavar o pellet com 500µl de etanol 70% e centrifugar por cinco minutos;

16- Descartar o álcool, com cuidado para não remover o pellet, e deixar amostra

aberta para evaporação total do etanol;

17- Após seco, ressuspender o pellet em 50 µl TE (Tris-EDTA, composição abaixo);

18- Incubar a 37°C por trinta minutos para ação da RNAse presente no TE;

19- Etiquetar a amostra contendo o DNA conservado em TE e manter em freezer a -

20°C para futura utilização;

20- Submeter a amostra a eletroforese em gel de agarose 1% para verificação de

presença e concentração do DNA.

Obs. Todas as extrações devem ser efetuadas utilizando-se material autoclavado e

descartável, e a manipulação realizada com jaleco laboratorial e luvas de látex para

procedimentos. Substâncias tóxicas devem ser manipuladas em capela, utilizando-se

máscara e óculos protetores.

VIII) APÊNDICE: MANUSCRITO: formatado para o periódico Molecular Ecology.

Green turtle (Chelonia mydas) mixed stocks in Southern Brazil, as revealed by mtDNA

haplotypes and ocean currents.

Maíra Carneiro Proietti

1,2

; Júlia Wiener Reisser

1,2

; Paula Lara-Ruiz

; Rodrigo Kerr

;

Danielle Monteiro

; Luis Fernando Marins

and Eduardo Resende Secchi

2,7

Programa de Pós-graduação em Oceanografia Biológica, Instituto de Oceanografia, Universidade

Federal do Rio Grande, Avenida Itália km8, CEP 96201-900, Rio Grande, RS, Brazil;

mairapr[email protected], jroceano@hotmail.com

Grupo de pesquisa Ecologia e Conservação da Megafauna Marinha – EcoMega

Grupo de Identificación, Unidad de Especies Silvestres, Instituto de Genética, Universidad Nacional de

Colombia. Ciudad Universitaria, Bogotá, Colombia; paula_lara@yahoo.com

Laboratório de Estudos dos Oceanos e Clima, Instituto de Oceanografia, Universidade Federal do Rio

Grande, Avenida Itália km8, CEP 96201-900, Rio Grande, RS, Brazil;rodrigoke[email protected]

Núcleo de Educação e Monitoramento Ambiental (NEMA), Rua Maria Araújo 450, CEP 96207-480, Rio

Grande, RS, Brazil; danismonteir[email protected]

Laboratório de Biologia Molecular, Instituto de Ciências Biológicas, Universidade Federal do Rio

Grande, Avenida Itália km8, CEP 96201-900, Rio Grande, RS, Brazil; dqmluf@furg.br

Laboratório de Tartarugas e Mamíferos Marinhos, Instituto de Oceanografia, Universidade Federal do

Rio Grande, Avenida Itália km8, CEP 96201-900, Rio Grande, RS, Brazil; edu.secchi@furg.br

Keywords: green turtle, foraging grounds, Southwestern Atlantic, mtDNA, mixed stock analysis,

surface drifters

Short running title: Green turtle mixed stocks in Southern Brazil.

Abstract

Genetic analyses have the potential to elucidate many aspects of juvenile green turtle

(Chelonia mydas) biology and ecology, such as foraging ground composition, hatchling dispersal

and migrations. To evaluate genetic structure and assess natal origins of mixed stocks in Southern

Brazil, we analyzed mitochondrial DNA control region sequences from Arvoredo Island (n =

115) and Cassino Beach (n = 101), comparing them to other mixed stocks and examining their

composition in terms of stocks (nesting areas) in the Atlantic Ocean. In order to compare natal

origin estimates (obtained through Bayesian Mixed Stock Analysis) with oceanographic data and

develop novel informative priors for this analysis, surface drifter trajectories in the Atlantic

Ocean were analyzed. Each study area presented twelve haplotypes, of which ten were shared at

extremely similar frequencies. Haplotypes CM-A8 and CM-A5 were most frequent, representing

respectively around 60% and 20% of samples from both areas, and remaining haplotypes

presented frequencies lower than 5%. Genetic structuring was not observed between the study

areas. Arvoredo Island and Cassino Beach also did not present structuring in relation to Ubatuba

and Rocas/Noronha, in the southwestern Atlantic, but were structured when compared to farther

feeding areas in Brazil, the Caribbean, and North America. Analysis of drifter trajectories

revealed that drifters from Ascension and Trindade Islands are dominant at the eastern coast of

Brazil. Informative priors developed for Mixed Stock Analysis did not greatly alter stock

estimates; we do, however, consider them to be ecologically more realistic. Ascension, Aves and

Trindade Islands, as well as Gulf of Guinea, were the main contributors to the Southern Brazil

mixed stocks. However, rookeries require adequate genetic characterization in order to provide

accurate estimated of natal origins, an analysis with important implications for the survival of this

species, since the reduction of impacts on mixed stocks along the coast will ultimately lead to the

conservation of reproductive stocks frequently thousands of kilometers away.

Introduction

The globally-distributed and endangered green turtle (Chelonia mydas) occupies various

ecological niches throughout its life cycle (Meylan & Meylan 1999, Bolten 2003, Godley 2003).

A general life pattern encompasses a juvenile oceanic phase, in which it is believed that young

turtles drift with ocean currents, a subsequent neritic phase, when animals reach a certain size and

recruit to coastal foraging grounds, and large-scale migrations between foraging and breeding

areas upon sexual maturity (Bolten 2003). Movements between foraging grounds, often long-

range, are also observed (Godley et al. 2003, Reisser et al. 2008). The complexity of this life

cycle and the large geographical and temporal distances involved make direct studies of these

animals difficult. Indirect approaches through molecular analyses can help elucidate many

aspects of their biology and behavior (Bowen 1995, Bowen & Karl 1997, Bowen & Karl 2007,

Avise 2007), such as paternity, mating systems, population structure, inter-rookery gene flow,

phylogeography, systematic, natal origins and homing Avise (2007).

Natal homing, in which female green turtles return to their birth site to reproduce, was

first hypothesized by Carr (1967), based on the observation that female green turtles are

phylopatric, that is, they return to nesting sites (rookeries) at varying degrees of precision

throughout subsequent nesting cycles (Carr 1967, Miller 1997, Formia et al. 2007, Lee et al.

2007). Despite difficult to test, this hypothesis has been revealed plausible through genetic

studies, which have demonstrated that mtDNA structuring occurs between rookeries, but overlap

in foraging areas (Allard et al. 2004, Bass et al. 2006, Bowen & Karl 2007). Based on the

assumption that such structuring exists, a Bayesian approach known as Mixed Stock Analysis

(MSA) has been increasingly applied for determining contributions of genetically structured

rookeries (stocks) to mixed sea turtle foraging aggregations (mixed stocks), employing

differences in relative frequency of genetic characters (especially mtDNA) between rookeries to

link feeding populations to their sources (Pella & Masuda 2001). This analysis frequently

presents high standard deviations and is based on the assumption that all sources have been

adequately sampled. This is often not the case, and many areas still present insufficient or even

lack genetic characterization (Avise 2007). Despite these potential biases, MSA can be useful for

quantitative estimates and qualitative descriptions of green turtle origins in foraging habitats, as

long as it is not over interpreted (Bowen & Karl 2007).

The relevance of identifying natal origins of mixed stocks for conservation lies in the fact

that rookeries, despite being generally independent reproductively, are linked at the non-nesting

phases of the female green turtle life cycle (Avise 2007). Therefore, impacts at foraging grounds

and migratory routes may affect many breeding stocks at different levels. Understanding these

origins, as well as determining possible migratory routes, is crucial for the elaboration of

management and conservation plans (Moritz 1994, Avise 2007, Bowen & Karl 2007).

Nevertheless, caution is needed when using MSA estimates to understand stock contribution in

feeding grounds, and when possible, compared and associated with other data. The Bayesian

approach to MSA allows the incorporation of informative priors for improving estimates, and

ecological data such as rookery population size and distance from source to mixture are

commonly employed based on the assumption that foraging ground composition may be related

to these factors. Hatchlings are considered by most authors as “pelagic”, dispersing almost

passively with ocean currents until reaching a certain size (Bolten 2003). Therefore,

oceanographic data such as surface drifter trajectories can be viewed as an indicative of early life

stage dispersal routes, and have potential to be incorporated as ecologically-informative priors in

MSA.

Examples of green turtle MSAs employing mtDNA data from foraging areas in the

Atlantic and Pacific Oceans can be seen in Bass et al. (2006), Bolker et al. (2007), Bjorndal &

Bolten (2008), and Dutton et al. (2008). There are four green turtle rookeries in the central and

western South Atlantic, listed in decreasing number of nesting females: Ascension Island,

Trindade Island, Rocas Atoll and Fernando de Noronha. Origins of Brazilian juvenile green

turtles have been described though mtDNA data using MSA for Rocas Atoll, Fernando de

Noronha, Ubatuba and Almofala (Bjorndal et al. 2006, Naro-Maciel et al. 2007), with the

consistent observation of prevailing contributions from Ascension Island, followed by smaller

(yet significant) contributions from Trindade and almost null contribution from Rocas Atoll and

Fernando de Noronha. Naro-Maciel et al. (2007) conclude that this pattern of contributions is

shaped by the prevailing ocean currents flowing near rookeries.

The role of ocean currents in sea turtle dispersal and migration has been thoroughly

discussed (see Luschi et al. 2003a). As cited previously, hatchlings are thought to rely on oceanic

currents for dispersal until recruiting to their coastal foraging zone, and data obtained through

satellite telemetry indicate that sea turtle movements and migrations are frequently shaped by

ocean currents (Luschi et al. 1998, 2003a, 2003b; Craig et al. 2004). Parallels between MSA and

ocean currents have been made for North Atlantic mixed stocks by Luke et al. (2004) and Bass et

al. (2006), in which it is assumed that the compositions of these foraging aggregations depend on

local major and minor current systems. For Brazil, it has been suggested that Ascension Island

hatchlings drift with major Equatorial currents towards South America, while hatchlings from

other rookeries may drift away with prevailing currents (Naro-Maciel et al. 2007). The large-

scale upper-layer (< 100 m) general circulation pattern which could influence sea turtle dispersal

in the South Atlantic, affecting the composition of Brazilian foraging areas, is characterized by a

dominating anticyclonic subtropical gyre. The westerly-bound Southern Equatorial Current

bifurcates at the South American continental shelf at approximately 10° S originating the

northern-bound North Brazil Current and the southern-bound Brazil Current (BC). The BC

travels southward alongside the coast until reaching the Subtropical Convergence Zone

(approximately 33-38º S), where it encounters the Falkland Current and separates from the coast

forming the eastern-bound South Atlantic Current (SAC). When it approaches the African

continent, part of the SAC flows to the Indian Ocean and part forms the northern-bound Benguela

Current, which in turn will form the CSE and complete the (Stramma & England 1999).

Considering that genetic studies potentially elucidate many aspects of green sea turtle

biology and ecology, including foraging ground composition, hatchling dispersal and migrations,

this study aimed at: a) determining genetic differences amongst southern Brazil foraging areas of

Arvoredo Island (AI) and Cassino Beach (CB) and other mixed aggregations in the Atlantic; b)

estimating contributions of different rookeries to the AI and CB mixed stocks; c) developing

novel informative priors for Bayesian Mixed Stock Analysis; d) assessing the effect of

incorporating different ecological priors in Bayesian Mixed Stock Analysis; and e) determining

possible dispersal patterns from rookeries to the studied foraging areas.

Materials and Methods

Tissue sampling

Samples were collected at Arvoredo Island (27º51‟S 48º26‟W), in Santa Catarina state (n

= 66), and Cassino Beach (from 31°21‟S 51°02‟W to 33°44‟S 53°22‟W), Rio Grande do Sul state

(n = 101). Arvoredo Island lies within the Arvoredo Marine Biological Reserve and presents

rocky shores with high diversity of benthic organisms, and frequent occurrences of green turtles

(Reisser et al. 2008). Cassino Beach is an extensive and continuous sandy beach composed of

predominantly unconsolidated substrate and few substantial hard substrates. Green turtles are

frequently observed stranded at this beach (Bugoni et al. 2001), but its exact role in the life cycles

of these animals is unknown. At Arvoredo Island, skin samples were collected using 5 mm

disposable biopsy punches from the flippers of individuals hand-captured through free and

SCUBA dives in expeditions carried out from July 2005 to April 2008. At Cassino Beach,

samples were collected using disposable scalpels from stranded live animals or carcasses found

washed ashore during beach surveys conducted from January 2005 to May 2007. All samples

were conserved in absolute ethanol and maintained at -20 ºC until DNA extraction. Sea turtle

sizes ranged from 33.5-83 cm (mean 49.2 cm) and 29-71.5 cm (mean 40.1 cm) curved carapace

length (CCC), respectively for Arvoredo Island and Cassino Beach.

Laboratorial procedures

Tissue samples were macerated employing conical-shaped plastic grinders in a Tris-HCl

lysis buffer containing Proteinase K, and submitted to digestion in an oven at 37 °C until

complete digestion (from five to 24 hours). DNA was extracted through DNAExtraction Kits

(Tissue – Bioamerica Inc.), or standard phenol:chlorophorm method with precipitation in

absolute ethanol (adapted from Hillis et al. 1996). Approximately 500 bp-fragments of the

mitochondrial DNA control region were amplified via polymerase chain reaction (PCR), using

primers LTCM1 and HDCM1 (Allard et al. 1994) or LTCM2 and HDCM2 (longer versions of

the prior primers, designed by Lahanas et al. 1994). PCR conditions for the first primers were as

follows: initial denaturation of 1‟ at 94 °C; 35 cycles of 30‟‟ at 94 °C, 1‟ at 50 °C and 1‟ at 72 °C;

and a final 5‟ extension at 72°C. For the latter primers, applied conditions were: initial

denaturation of 1‟ at 94 °C; 35 cycles of 45‟‟ at 94 °C, 30‟‟ at 55 °C and 45‟‟ at 72 °C; and a final

3‟ extension at 72°C. Illustra GFX purification kits (GE Healthcare, U.S.A.) were employed for

purification, and samples were sequenced in both directions using DYEnamic ET dye terminator

kit in a MegaBACE 500 DNA sequencer (GE Healthcare, U.S.A.).

Data analysis

mtDNA sequences

Sequences were aligned using software Clustal X 1.83 (Thompson et al. 1997), and

haplotypes (491 bp, according to previously-described haplotypes for Chelonia mydas) classified

according to the Archie Carr Center for Sea Turtle Research online genetic bank (Florida

University). Additional sequences for Arvoredo Island (n = 49, Proietti et al. in press) were

included in the analyses, totalizing 115 samples. Relationships among haplotypes were

demonstrated through a statistical parsimony network, constructed using TCS 1.3 software

(Clement et al. 2000). Exact tests of differentiation were conducted with Arlequin 3.11 (Excoffier

et al. 2005) in order to verify differences between the study areas and other previously-described

Atlantic foraging grounds, employing a Markov Chain Monte Carlo (MCMC) of 10000 steps

with 1000 dememorizations (“burn-in”). This software was also used to calculate pairwise Φ-

statistics for an Analysis of Molecular Variance (AMOVA), with 10000 permutations and using

the Tamura-Nei model of nucleotide substitution. The Brazilian foraging grounds included in

these analyses for comparative purposes were Ubatuba (SP), Almofala (CE) (Naro-Maciel et al.

2007), and Rocas Atoll (RN) and Fernando de Noronha (PE) (Bjorndal et al. 2006). The last two

were grouped into one unit for all analyses due to geographic proximity (c.a. 150 km) and small

sample sizes, as performed in Bjorndal et al. (2006), and will hereafter be referred to as

Rocas/Noronha. Nicaragua (Bass et al. 1998), Barbados (Luke et al. 2004), Bahamas (Lahanas et

al. 1998), Florida (Bass and Witzell 2000) and North Carolina (Bass et al. 2006), in the

Caribbean and North Atlantic, were also considered for comparison. Genetic structure results

were taken into consideration when defining certain aspects of analyses of surface drifter data

and Mixed Stock Analyses (see details below).

Surface drifter trajectories and natal origins

In order to compare MSA results with oceanographic data and develop two novel

informative priors for MSA analysis, surface drifter data available for the Atlantic and

Mediterranean (5842 drifters, from February 1979 to January 2009), was downloaded from

NOAA‟s Global Drifter Program (http://www.aoml.noaa.gov/envids/gld). In order to develop

these new priors, the information provided by drifters was summarized as the number of drifters

which pass through the nesting areas considered in the natal origin analyses (n

, j = 1, …, 11) and

reach a target area (y

) consisting in the eastern Brazilian coast, from the southernmost limit to the

northeastern corner. The northern portion of the country was not included due to evidence of

genetic structuring between this area and the coast of East Brazil.

The nesting areas considered as possible sources (AF

) correspond to all rookeries in the

Atlantic and Mediterranean with mtDNA description, as reported by Encalada et al. (1996),

Kaska (2000), Bjorndal et al. (2005, 2006), Formia et al. (2006, 2007): (1) Trindade Island, (2)

Rocas/Noronha (Brazil), (3) Ascension Island (United Kingdom), (4) Poilão (Guiné Bissau), (5)

Bioko Island (Equatorial Guinea), São Tomé and Príncipe (Democratic Republic of São Tomé

and Príncipe), (6) Aves Island (Venezuela), (7) Matapica (Surinam), (8) Quintana Roo (Mexico),

(9) Tortuguero (Costa Rica), (10) Florida (United States) and (11) Lara Bay (Cyprus). Bioko, São

Tomé and Principe were grouped into one area due to proximity and lack of genetic

differentiation, and hereafter will be referred to as Gulf of Guinea, following Bolker et al. (2007).

4°x4° areas were delineated around all these considered rookeries, and in the case of non-insular

rookeries, the area was designed in order to incorporate the largest possible oceanic area.

In order to estimate the probability (



) that a drifter passing through an AF

will reach

the target area, a uniform prior between zero and one was used for



(uninformative prior) in a

binomial sampling model:

 

jjjjj

nyBinyp







(1)

Applying Bayes‟ Theorem:

 

jjjjj yppy





to equation 1, we obtain a

posterior density following a Beta distribution:

 

1,1~  jjjjj ynyBetay



(2)

By estimating posterior values of the Beta probability distribution parameters for each

source area, random values following posterior distributions of



can be generated. Here, 2500

values were generated following the distribution shown in Equation 2. Since we are interested in

the probability

that a new success (that is, a drifter reaching the Brazilian coast) is from

jAF

the posterior distribution of the variable must be considered, corresponding to:







(3)

Bayes estimates under quadratic loss (average of the 2500 values generated for each

posterior distribution) were chosen for point estimates of

. When considering that







equation 3 is modified to:







(4)

These average values were directly used as an informative prior in the MSA. Additionally,

another prior was developed and used in the MSA, by using the average values cited above and

the number of females of each rookery (

), through the equation:









(5)

Probable natal origins were determined employing mtDNA data from the study areas and

all rookeries with mtDNA description (see above), through Bayesian Mixed Stock Analysis

(MSA) implemented with software Bayes (Pella & Masuda 2001). Arvoredo Island and Cassino

Beach were grouped into one area due to geographic proximity and genetic similarity, and four

MSAs were performed considering uninformative priors (MSA

) and priors weighed according

to: posterior probabilities calculated from surface drifter data (MSA

– equation 3); number of

females/year of each source (MSA

); and a combination of the two previous informative priors

(MSA

– equation 5). Source populations considered as possible contributors to the study areas

correspond to the same area used in surface drifter analysis. One MCMC was implemented for

each rookery (totalizing 11 chains) in each analysis, with chain lengths varying from 10000 –

25000, according to the Gelman-Rubin convergence factor (which was maintained under 1.2, and

in most cases presented values of approximately 1.0, indicating convergence) and one-half chain

length discarded as “burn-in” steps (as described by Pella & Masuda 2001).

Results

Haplotype frequencies and genetic diversity

Each study area presented 12 previously-described haplotypes, of which ten were shared

(CM-A5, CM-A6, CM-A8, CM-A9, CM-A10, CM-A23, CM-A24, CM-A32, CM-A42 and CM-

A45) and four were not shared (CM-A3 and CM-A39, present only at AI, and CM-A25 and CM-

A36, present only at CB) (Table 1). Both areas were characterized by a high predominance of

haplotypes CM-A8 (61% for both areas) and CM-A5 (22% and 20% for AI and CB,

respectively). All remaining haplotypes were present in frequencies lower than 5%. Rare

haplotypes were observed, such as CM-A10, CM-A23 and CM-A24, encountered only at

Ascension and Trindade islands; CM-A25 and CM-A32 only at Rocas Atoll and Ascension

Island; CM-A39 and CM-A45 at Ascension Island; and CM-A42 in only two individuals at the

Almofala foraging ground in northeast Brazil, with no observations in rookeries. The number of

polymorphic sites defining these haplotypes was 19 for AI and CB, with a maximum of 12

variations distinguishing them (Figure 1).

Haplotype (h) and nucleotide (π) diversities of AI (h = 0.5831 ± 0.0451; π = 0.00246 ±

0.00176) and CB (h = 0.5857 ± 0.0501 and π = 0.00251 ± 0.00178), and the averaged diversities

of all compared foraging aggregations (h = 0.5410; π = 0.0045), were similar, as shown in Table

Genetic differentiation

Exact test of differentiation and AMOVA revealed an overall structuring among foraging

areas (p < 0.001 for both analyses); however, genetic structuring was non-significant between AI

and CB, with a slightly negative Φ

value (Φ

= -0.0066, p > 0.05), indicating that the actual

value is very small (Weir 1996). Both analyses revealed that AI and CB are genetically different

from most areas (Almofala, Nicaragua, Barbados, North Carolina, Florida and Bahamas, p <

0.05), but showed no difference in relation to Ubatuba and Rocas/Noronha (p > 0.05), both

located in the Southwestern Atlantic.

Surface drifter trajectories and natal origins

Drifters coming from Ascension and Trindade Islands are dominant at the target area, as

clearly shown in Figure 2, which illustrates the trajectories of all surface drifters which passed

through the eleven 4°x4° areas (all rookeries considered in the MSAs). Table 3, which lists the

total number of drifters which passed through each area, the number of these that reached the

target area, and the point estimates of for each rookery, shows that only Ascension, Trindade,

Rocas/Noronha and Gulf of Guinea supplied drifters to the target area. The first two areas

presented

of around 40%, while Rocas/Noronha and Gulf of Guinea presented

of near 2%

and slightly over 5%, respectively. Although Costa Rica and Guinea Bissau exhibited posterior

probabilities of over 2%, they are not considered relevant due to the fact that this estimate is

simply a result of the small number of drifters passing through the areas. The remaining rookeries

presented probabilities lower than 1%.

All MSAs for Arvoredo Island and Cassino Beach indicate that the main contributors to

the southern Brazil foraging areas were Ascension, Aves and Trindade Islands (Figure 3).

Ascension Island consistently presented the largest contributions, ranging from 53.3 to 66.5% in

the four performed MSAs, while Aves and Trindade Islands exhibited contributions that ranged

from 21.6 to 22% and 7.6 to 17.7%, respectively. Remaining stocks presented low contributions

in all MSAs (less than 1% in a general manner), with the exception of the Gulf of Guinea, with

estimated contributions from 2.1 to 7.3%.

MSA

(which used surface drifter data as ecological information) slightly increased

Ascension Island contribution estimates when compared to the uninformative MSA

, while

MSA

(prior weighing rookery population size) and MSA

(combination of both ecological

priors) increased estimates in slightly over 12% for the former and 6% for the latter analysis.

Contributions from Trindade Island increased to 17.7% in MSA

, while MSA

decreased this

contribution to slightly less than 8%. Gulf of Guinea‟s contributions in MSA

was relatively high

(around 7%), but decreased to 5.2% in MSA

, and when inserting the ecological variable rookery

size (MSA

) and the combination surface drifters/rookery size (MSA

), contribution from this

stock dropped to 2.1%. Of the largest contributors, Aves Island was the least variable throughout

MSAs, varying less than 1%.

Discussion

Haplotype frequencies of Arvoredo Island and Cassino Beach were similar to other

Atlantic rookeries and foraging aggregations: high CM-A8 frequency, consistent with the

suggestion that this haplotype is the closest relative to an ancestral haplotype in the Atlantic

basin, followed by a high occurrence of CM-A5, an extremely common haplotype encountered in

Caribbean rookeries (Bjorndal et al. 2005, 2006; Formia et al. 2006, 2007; Naro-Maciel et al.

2007), and low frequencies of rarer haplotypes. Increasing sample size did not significantly

change the proportion of haplotypes CM-A8 and CM-A5 or the diversity indexes found by

Proietti et al. (in press) at Arvoredo Island. The detection of rarer haplotypes, however, increased.

High haplotype and low nucleotide diversity indexes for both study areas followed the general

pattern found at other green turtle foraging grounds due to the mixed characteristic of these areas

and small variations between haplotypes, respectively (Bass et al. 2006; Bjorndal et al. 2006;

Naro-Maciel et al. 2007).

Arvoredo Island and Cassino Beach were extremely similar in terms of haplotype

diversity and frequency, and were not significantly different from other foraging grounds in the

southwestern Atlantic (Ubatuba and Rocas/Noronha). A significant structuring, however, seems

to occur in relation to Almofala, a more distant feeding area located in northeast Brazil. This area

presents larger frequencies of Caribbean haplotypes, explaining such differentiation, as the

Caribbean region presents elevated mtDNA structuring within the Atlantic basin (Bass et al.

2006). Juvenile sea turtles may perform coastal migrations, sometimes seasonal, transiting

between foraging areas according to different factors (such as variations in current intensity,

water temperature, and food availability (Musick & Limpus 1996, Bass et al. 2006). Avens &

Lohmann (2004) studied seasonal movements of green turtles in North Carolina, and reported

that animals swam in opposite directions according to the season: northwards in the summer and

southwards in the autumn. Green turtles tagged in Uruguay have been recaptured in Brazil and

vice-versa, suggesting that some juveniles perform seasonal movements, going to lower latitudes

during colder periods and to higher latitudes during warmer seasons (Lopéz-Mendilaharsu et al.

2006). The possibility of inter-annual movements, or of even longer intervals, cannot however be

discarded. Souza & Robinson (2004) demonstrated through Langrangian measurements and

analysis of Sea Surface Temperature images that the intrusion of cold waters transported by a

coastal current is apparently a regular winter phenomenon occurring on the Brazilian shelf, at

latitudes up to around 25° S. This intrusion was so consistent that these authors named it the

“Brazilian Coastal Current”, and could favor coastal movements from Uruguay to Brazil during

cold periods. The Brazil Current, on the other hand, could facilitate opposite displacement

patterns. Musick & Limpus (1996) speculated that juveniles at temperate zones perform these

seasonal movements in order to seek warmer waters and avoid cold stunning. A four-year study

performed at Arvoredo Island revealed moderate site fidelity of immature green turtle site

fidelity; however, one turtle tagged at the island was found six months later stranded on a beach

in the state of São Paulo, over 600 km away (Reisser et al. 2008). For Cassino Beach, evidence of

residency is not available, and it is possible that some animals are in fact from distant areas and

simply pass through or perish, drift and strand on the over 350 km stretch of sandy beach. This

indicates that the area may be not only a foraging ground, but also a migratory corridor for this

species. Observations of juvenile sea turtle coastal movements have demonstrated that the genetic

similarity between proximal coastal feeding areas is in accordance with the movements

performed by these animals (Marcovaldi et al. 2000).

Surface drifter trajectories presented in Figure 2 clearly reveal that Ascension and

Trindade Islands, and at a lesser extent Gulf of Guinea and Rocas/Noronha, present favorable

conditions for conducting drifters to the eastern Brazilian coast. As highlighted before, ocean

currents are considered by most authors to influence sea turtle dispersal and migration, but direct

evaluations have been performed only for post-pelagic animals. Craig et al. (2004) compared the

post-nesting migration routes of female green turtles satellite-tagged while nesting at Rose Atoll

(Pacific Ocean) with surface drifter data. These authors found that, even though their means of

navigation were not investigated, the migration routes undergone by the females closely

paralleled surface ocean currents. Luschi et al. (1998) verified relationship between post-nesting

movements of female green turtles satellite-tagged at Ascension Island and prevailing ocean

currents, by employing a general circulation model (global isopycnic model). They noted that the

turtles initially followed directions highly coincident with the prevailing current at the given

period. For satellite-tagged female leatherback turtles, post-nesting movements were monitored

and paralleled to surface drifter data (surface current patterns) (Luschi et al. 2003b). Large

portions of the females‟ routes were strikingly similar to those of surface drifters tracked in the

same region, and the authors concluded that long-lasting oceanic movements of marine turtles

may be shaped by oceanic circulation patterns. Although such studies cannot be performed with

hatchling sea turtles due to their small size and evidence of a passive pelagic stage is mostly

indirect, it is reasonable to suspect that sea currents play an important role in the movements of

hatchlings and early juveniles of all sea turtle species (Luschi et al. 2003b).

As observed in the Brazilian sea turtle foraging grounds described by Naro-Maciel et al.

(2007) the main stock contributing to the studied areas was Ascension Island (Figure 3). Large

contributions from Ascension Island were also estimated by Bjorndal et al. (2006) when studying

the mtDNA of a small sample (n = 31) from the Rocas/Noronha mixed stock. For the Gulf of

Guinea foraging aggregation, Formia (2002) also found that the highest contributions were from

Ascension Island, but followed by almost equal contributions from rookeries located at the

Guinea area. Differently, MSA estimates of a foraging aggregation in North Carolina (Bass et al.

2006) did not reveal contribution from Ascension, being composed mainly of rookeries located in

the United States, Mexico and Costa Rica, which is in accordance with the marked mtDNA

structuring of the Caribbean region within the Atlantic Ocean. Aves Island was the second most

important contributor in MSAs for AI and CB, followed by Trindade Island. Such high

contribution from Aves Island (approximately 20% in most MSAs) was not observed for other

feeding areas in Brazil, which presented a maximum contribution of 18% for Almofala,

reasonable when considering that this area is located closer to the Aves rookery than to other

nesting sites. The low contributions from African and North-American rookeries are in

accordance with Naro-Maciel et al.‟s (2007) findings for Ubatuba and Almofala. These authors

detected a relatively high contribution (around 10%) only from Guinea Bissau to Ubatuba, but

this contribution was considered possibly flawed due to the fixed characteristic of this rookery for

haplotype CM-A8. Gulf of Guinea‟s contribution to the study areas, however, was noticeable in

MSA

and MSA

(7.2 and 5.3%, respectively), and relatively high in the remaining MSAs

(approximately 2%) when compared to other rookeries. Contributions from Cyprus were null or

almost so in all MSAs, in accordance with all other studies quoted above and with the hypothesis

that Mediterranean green turtles were recently separated from their relatives in the Atlantic Ocean

(Kaska 2000).

The link between Brazil and Ascension Island has long been disclosed by tagging and

telemetry studies of female green turtles (Meylan 1995; Luschi et al. 1998; Hays et al. 2002).

There is no evidence of movements between Aves Island (second largest contribution to southern

Brazil as estimated by MSA) and Brazilian foraging grounds, but other Caribbean rookeries have

been shown to be linked to North Brazil (Lima et al. 2008). Marcovaldi et al. (2000) reported

frequent recaptures along the Brazilian coast (from latitudes 03°45‟S to 20°08‟S) of female green

turtles tagged at Trindade Island, demonstrating that movements between this island and coastal

foraging grounds are common. Although green turtles tagged at Trindade have also been

recaptured in Western Africa (Marcovaldi et al. 2000), transatlantic movements between Brazil

and the west coast of Africa have not yet been confirmed by tagging programs or telemetry.

Tagging efforts however have demonstrated such movements for hawksbills (Bellini et al. 2000,

Grossman et al. 2007) and leatherbacks (Billes et al. 2006). Despite the scarcity of drifter data for

the Gulf of Guinea area, a certain tendency of buoy to drift towards the Western Atlantic can be

noted (Figure 2). Two possible explanations for the lack of evidence of a green turtle Brazil-

Africa link can be suggested: 1) green turtles born in the Atlantic coast of Africa rarely migrate to

Brazil for foraging, and vice-versa; and 2) the limited amount of studies at the African continent

has not yet revealed such migrations. In any one of these scenarios, further genetic and

demographic studies at the western coast of Africa are necessary for enlightenment of green turtle

dispersal in the Atlantic.

Despite the fact that the Ascension contribution is apparently disproportionately large

when compared to other rookeries, it could in fact be reasonable. As demonstrated by drifter

trajectories, surface currents favor dispersal from Ascension towards the Brazilian coast, which is

also observed for Trindade Island and Rocas/Noronha. Considering that contribution is

theoretically proportional to the population size of nesting females, it would be natural to expect

a larger contribution from Ascension Island, as this is the most populated of the South Atlantic

rookeries and the second largest in the Atlantic Ocean (approximately 3800 females nesting

annually, Broderick et al. 2006). Various authors have demonstrated that a West-Southwest flow

of the South Atlantic Equatorial Current is a common feature at the Ascension area (Luschi et al.

1998, Hays et al. 2002). Thus, it is reasonable to suggest that hatchlings arrive at the South

American coast by means of this favorable current.

Few drifters left the Rocas/Noronha area and moved southwards along the Brazilian coast

(Table 3); conditions are apparently favorable for drifting northward along the Brazilian coast, as

shown in Figure 2, probably due to the strong North Brazil Current. These rookeries together

present a small population of nesting females of approximately 100-150/year (Seminoff 2002),

which could also be a possible explanation for its low estimated contribution. Aves Island was

not shown by drifter data to be linked to Brazilian waters, but presented large contribution as

estimated by MSA. The high frequency (90%) of CM-A5 at this rookery and frequent occurrence

of this haplotype at AI and CB is a probable cause for such estimation, which should be

interpreted with caution. Despite presenting a relatively large nesting population (300-500

females nesting annually; Seminoff 2002), the number of analyzed samples is fairly low (n = 30),

and contribution could be overestimated. Haplotype CM-A5 is present at some other rookeries in

low frequencies (Mexico, Costa Rica and Gulf of Guinea), and composed 86% of the observed

haplotypes in a small sample (n = 15) from Surinam. Contributions from these areas however

were almost zero, and the reason why Aves presented such high contribution needs further

investigation. Trindade Island is the second largest rookery in the South Atlantic, with between

600-800 females/year over the last nesting seasons (Soares L, pers comm). It is also close to the

Brazilian coast, and clearly presents favorable ocean currents for arrival at the target area (see

Figure 2). Some factors may lead to a negative influence on the number of green turtles reaching

the coast. It is possible that at Trindade female fecundity and egg eclosion success is lower and

hatchling and small juvenile mortality due to predation and current transportation to low

temperature areas is higher than in other areas, lowering the likelihood of arrival. When

compared to other South Atlantic rookeries for example, Trindade is located at a relatively high

latitude, and in Figure 2 it can be noted that some buoys passing through Trindade drift to higher

latitudes, and consequently lower temperature, areas. Another possibility raised by Figure 2 is

that green turtles born at Trindade may arrive at the Brazilian coast at a size too small for

recruitment and drift with the South Atlantic Current to the African coast, or even be carried

directly from Trindade to West Africa, where they may recruit. Such factors require further

investigation in order to verify their relevance. In any case, we believe that Trindade‟s

contribution could be underestimated, and suggest additional genetic analysis of this rookery and

verification of mixed stock composition in southwest Africa, as well as the elaboration of

different statistical approaches to MSA. An example of a novel approach to the analysis of mixed

stock compositions is the „many-to-many‟ MSA (Bolker et al. 2007), which has demonstrated

that incorporating multiple mixed stocks in the analysis might modify contribution estimates. For

northeastern Brazil, these authors reported that this analysis greatly altered results, increasing

estimates from Trindade Island and decreasing contributions from Ascension and Aves Islands.

As stated above, MSA may present wide confidence limits and some limitations. The available

mtDNA markers cannot precisely distinguish all rookeries, and interpretation of results must take

this into account by assuming regional origins as opposed to exact nesting beaches (Avise et al.

2007). Also, MSA assumes that all nesting areas have been sampled, when in fact many lack

genetic studies and many others present small sample sizes (see Table 1). The presence of

“orphan” haplotypes, that is, haplotypes which have been encountered in foraging grounds but

not in nesting ground samples (Bolker et al. 2007), confirms this statement. The most commonly

employed software for MSA estimates, BAYES, requires removal of such “orphans”, which leads

to the exclusion of rare haplotypes, such as haplotype CM-42, encountered until today only at

Almofala and at Arvoredo Island and Cassino Beach.

One of the advantages of analyzing data in a Bayesian framework is the possibility of

incorporating previous knowledge as informative priors. Reproductive data can be extremely

useful ecological priors in MSA, altering estimates by providing the proportion of hatchlings that

each area contributes to sea (i.e., the larger the nesting population size, the higher the potential

contribution). Employing data such as number of hatchlings that effectively reach the ocean

(hatchling survival) could be useful, but unfortunately reproductive data collected at nesting areas

are not standardized. Due to the fact that the most commonly available data is the number of

nesting females per year, we acquiesced to adopt this information for representing the

reproductive population size. This information, however, is often biased. For example, Bjorndal

et al. (2006), Bolker et al. (2007) and Naro-Maciel et al. (2007) considered as 3000 the number of

females nesting annually at Trindade Island (according to Seminoff 2002), which is a highly

overestimated value. Therefore, it is important to standardize the collection of such data and

provide updated, reliable estimates of rookery size, in order to develop more realistic ecological

priors. Another frequently used ecological prior is distance from source to mixture, in the form of

great circle distances. These distances are simply a measure of the shortest geodesic route from

one point to the next, and do not represent actual travelled distances, especially when considering

ocean currents, with their meanders, eddies and circulation patterns. Naro-Maciel et al. (2007)

noted that this type of distance was not significantly related to contribution, and we suggest that

the surface drifter trajectories used in this work is probably more informative for MSA when

considering the life history traits of sea turtles.

Some caveats exist in considering surface drifter data as ecological information for green

turtles: surface buoys present 15 m drogues to measure mixed layer currents (Lumpkin & Pazos

2007) and therefore do not consider surface wind drag which could influence hatchling

movement; data were not limited to the hatching seasons of turtles, due to the large reduction in

number of available drifter data; and the life-span of a drifter (approximately 400 days, see

Lumpkin & Pazos 2007) is generally shorter than the pelagic phase of hatchlings. However, the

use of such data has the potential to provide a more thorough understanding of sea turtle dispersal

and the role of ocean currents in that dispersal, and greatly improve traditional distances inserted

as priors in MSA. We suggest that future MSA analyses further advance prior development,

modeling the afterlife of drifters and considering the effect of wind drag on surface currents, and

consequently, hatchling dispersal.

The insertion of ecological priors did not greatly alter MSA estimates (Figure 3). When

using rookery size (number of females/year) as an ecological prior for MSA of Rocas/Noronha,

Bjorndal et al. (2006) found a decrease in contributions from Ascension and Aves Islands and an

increase in contribution from Trindade. For Ubatuba and Almofala insertion of the same prior

increased contribution estimates from Ascension and Trindade Islands, while the Aves Island

contribution decreased (Naro Maciel et al. 2007). However, as mentioned above, these authors

overestimated the number of females nesting annually at Trindade, and therefore such alterations

in stock estimates are unreliable. Bass et al. (2006) reported noticeable alterations when

incorporating population size estimated in MSA of the North Carolina foraging area, and

concluded that such estimates are biologically more truthful than estimates obtained with

uninformative priors. In the present work, we believe that MSA

, which incorporated the

combination of different types of ecological information, is the ideal scenario for obtaining

realistic stock contribution estimates. Nonetheless, the combination of all priors performed in

MSA

resulted in estimates quite similar to the uninformative MSA.

Arvoredo Island and Cassino Beach were very similar in terms of green turtle mtDNA,

and were grouped for Mixed Stock Analysis. Despite being genetically indistinct units, we

suggest that different management strategies be adopted due to some differences in the

characteristics of green turtles occurring at the areas, as well as their use of the habitats. Mean

curved carapace size of sampled animals revealed that green turtles at AI and CB are at different

stages of their life cycles, with CB hosting smaller animals than AI. This is in accordance with

sizes registered by Bugoni et al. (2003) and Reisser et al. (2008), of respectively 37.7 cm and

50.1 cm for CB and AI. These different stages present different vulnerabilities and their survival

influences population growth rates in different manners, with larger juveniles, due to their higher

reproductive value, contributing more than small individuals (Crouse et al. 1987; Crouse et al.

1999). Also, these habitats may represent different roles in the life cycles of juvenile green

turtles. While at least short-term fidelity has been observed at Arvoredo Island, the role of

Cassino Beach is still unclear due to lack of mark-recapture or telemetry studies, and it is

possibly more important as a migratory corridor than a feeding area when compared to Arvoredo

Island, perhaps due to factors such as lower food availability and temperatures. Marine turtle

bycatch in fisheries is today one of the major obstacles for the recovery of populations reduced

by overexploitation and habitat degradation, and in Southern Brazil, it has been evidenced that

sea turtle mortality due to fishery interaction, as well as ingestion of human debris, is an issue of

concern (Bugoni et al. 2001, Domingo et al. 2006). Such impacts on the developmental stages of

green turtles prevent the fulfillment of their ecological role of reaching maturity and reproducing,

and the conservation of juveniles along the coast leads to the protection of rookeries which are

frequently thousands of kilometers away (Naro-Maciel et al. 2007). The highly migratory

behavior of green turtles, which may occupy the waters of many countries as demonstrated by

demographic and genetic studies, makes international cooperation essential for the conservation

of these animals. The identification of stock contributions to mixed aggregations, through MSA,

has important conservation implications, and if is to be seriously considered as a tool for the

adequate elaboration of conservation and management plans, it is necessary that nesting

populations be adequately described in terms of mtDNA, in order to provide complete and

accurate baseline genetic data for estimates of natal origins.

Acknowledgements

We greatly acknowledge Pata da Cobra Diving and the Brazilian Navy for logistic support

in expeditions, and all personnel involved in field work. We acknowledge Projeto Tamar-

ICMBIO for partnership, and Núcleo de Educação e Monitoramento Ambiental (NEMA) and

Centro de Recuperação de Animais Marinhos (CRAM) for samples from Cassino Beach (with

special thanks to Alice Leite). The authors also thank Liane Artico, Daniela Volcan, Ana

Studzinsky for generous laboratorial aid, and Sarah Vargas and Fabrício Santos, as well as the

Laboratório de Biodiversidade e Evolução Molecular (UFMG) staff for help and partnership in

sequencing. The authors have received financial support from the Conselho Nacional de Pesquisa

(CNPq – Brazil), Rufford Small Grants (RSG – UK) and The People‟s Trust for Endangered

Species (PTES – UK).

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Figures

Figure 1. Minimum Spanning Network of haplotypes encountered at Arvoredo Island and

Cassino Beach. Black bars represent 1 basepair substitutions between haplotypes.

Figure 2. Surface drifter trajectories in the Atlantic Ocean, with study areas (black circles),

rookeries (4°x4° squares), and target area. Letters stand for Rocas/Noronha (R/N), Ascension

Island (AS), Trindade Island (TR), Guinea Bissau (GB), Gulf of Guinea (GG), Aves Island (AV),

Mexico (MX), Costa Rica (CR), Suriname (SU), Florida (FL), Cyprus (CY).

Figure 3. Mixed Stock Analyses estimates for the southern Brazil foraging aggregations, with

C.I.s and weights of employed priors. MSA

– uninformative prior; MSA

– prior reflecting

surface drifter data; MSA

– prior reflecting number of females nesting per year at each rookery;

MSA

– prior constructed to weigh both previous priors. Prior weights are represented in black,

and MSA estimates in gray.

Figure 1

Figure 2

Figure 3

Tables

Table 1. Haplotype frequencies for all cited foraging and nesting areas in the Atlantic, with total number

of haplotypes and samples per area. Letters stand for: Arvoredo Island (AI), Cassino Beach (CB), Ubatuba

(UB), Almofala (AF), Rocas/Noronha (R/N), North Carolina (NC), Nicaragua (NI), Bahamas (BH),

Florida (FL), Barbados (BB), Ascension Island (AS), Trindade Island (TR), Guinea Bissau (GB), Gulf of

Guinea (GG), Aves Island (AV), Mexico (MX), Costa Rica (CR), Suriname (SU), Cyprus (CY).

Foraging grounds Rookeries

Haplotype

R/N

h,i,j

R/N

h,l

CM-A1

CM-A2

CM-A3

395

CM-A4

CM-A5

CM-A6

CM-A7

CM-A8

204

CM-A9

CM-A10

CM-A11

CM-A12

CM-A13

CM-A14

CM-A15

CM-A16

CM-A17

CM-A18

CM-A20

CM-A21

CM-A22

CM-A23

CM-A24

CM-A25

CM-A32

CM-A33

CM-A35

CM-A36

CM-A37

CM-A38

CM-A39

CM-A42

CM-A44

CM-A45

CM-A46

CM-A50

CM-A55

N. hap.

N. samples

115

101

113

117

245

433

Naro-Maciel et al. 2007,

Bjorndal et al. 2006,

Bass et al. 2006,

Bass et al. 1998,

Lahanas et al. 1998,

Bass &

Witzell 2000,

Luke et al. 1994,

Encalada et al. 1996,

Formia et al. 2006,

Formia et al. 2007,

Bjorndal et al. 2005,

Kaska 2000.

Table 2. Haplotype (h) and nucleotide (π) diversity estimates ± standard deviations for all

compared foraging aggregations. For references see Table 1.

Foraging ground

Arvoredo Island

0.5831 ± 0.0451

0.0024 ± 0.0017

Cassino Beach

0.5857 ± 0.0501

0.0020 ± 0.0015

Ubatuba

0.4460 ± 0.0556

0.0021 ± 0.0016

Rocas/Noronha

0.5887 ± 0.0911

0.0019 ± 0.0015

Almofala

0.7168 ± 0.0306

0.0067 ± 0.0039

Barbados

0.7734 ± 0.0276

0.0105 ± 0.0057

Bahamas

0.3703 ± 0.0650

0.0066 ± 0.0038

Nicaragua

0.1831 ± 0.0621

0.0039 ± 0.0025

Florida

0.4855 ± 0.0668

0.0032 ± 0.0021

North Carolina

0.6778 ± 0.0310

0.0052 ± 0.0031

Average

0.5410

0.0045

Table 3. Global drifter data from the Atlantic Ocean and ecological priors for MSA. N = total

number of drifters per 4°x4° area; Y = number of drifters reaching the target area of the Brazilian

coast; P

= posterior probability that a drifter that arrived at the target area is from a given rookery.

Stock

Ascension Island

0.410

Trindade Island

0.424

Rocas/Noronha

140

0.017

Gulf of Guinea

0.053

Guinea Bissau

0.033

Cyprus

0.003

Costa Rica

0.021

Surinam

0.009

Mexico

0.009

Aves Island

106

0.008

Florida

195

0.004

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